Gastric Cancer
Conditions
Keywords
gastric cancer, artificial intelligence, endoscopy
Brief summary
This study aims to construct a real-time quality monitoring system based on artificial intelligence technology.
Detailed description
Gastroscopy plays an important role in the detection and diagnosis of upper gastrointestinal diseases. It is necessary for endoscopists to operate gastroscope according to the standardized process, in order to avoid missing early lesions. However, with the rapid increase in the number of endoscopies, the workload of endoscopists increases further. High workload reduces the quality of endoscopy, resulting in incomplete observation of anatomical parts that are easy to be missed in the process of gastroscopy. There are significant differences in the operation level of different endoscopists. Therefore, carrying out artificial intelligence methods has good academic research and practical value for improving the quality of endoscopic diagnosis and treatment. Artificial intelligence devices need to use a large number of endoscopic images, based on this, we intends to collect endoscopic image data from our hospitals for training and validation of the model.
Interventions
DIAGNOSTIC_TESTblind spots
missed part during map the entire stomach through endoscopy
Sponsors
Peking University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
Yes
Inclusion criteria
1. Patiens aged 18 years or above undergoing gastroscopy； 2. Be able to read, understand and sign informed consent；
Exclusion criteria
1. Patients with absolute contraindications to endoscopy examination; 2. pregnant women; 3. previous history of gastric surgery; 4. the researcher considers that the subject is not suitable for clinical trial.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy | 2020.2.22-2020.7.1 | Calculate the accuracy of AI's judgment on images |
| Sensitivity | 2020.2.22-2020.7.1 | number of images in which AI correctly diagnosed positive/all images with positive |
| Specificity | 2020.2.22-2020.7.1 | number of images in which AI correctly diagnosed negative/all images negative |
Countries
China
Outcome results
None listed